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are also developing novel machine learning methods to improve risk gene prediction and variant interpretation. This role will focus on the analysis of large-scale human genetics, scRNAseq, and proteomics
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statistical methods to agronomic research, including mixed models, geospatial statistics, multivariate analysis, and machine learning - Must possess and maintain an active and valid driver’s license Preferred
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, the Associate will assist with research and development projects in the areas of robotics, machine learning, control systems, and/or computer vision. They will be responsible for: designing and analyzing
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, algorithms or/and machine learning, through publications or conferences. - Strong communication skills. Overtime Status Exempt: Not eligible for overtime Appointment Type Restricted Additional Information
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and great opportunity of interdisciplinary training in machine learning and functional genomics. The project combines cutting-edge computational approaches, especially state-of-the-art machine learning
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. Minimum Requirements: The position requires a PhD in biological sciences, chemistry, or a related discipline and a strong record of publications in peer-reviewed journals. Preferred Qualifications: Ideal
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provides robust learning experiences related to the science and application of HEOR, including cost studies, data analytics, economic models, real-world evidence, services utilization analysis, systematic
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understanding of artificial intelligence applications and methodologies, such as working knowledge of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI
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translational research involving multimodal neuroimaging analyses, statistics, machine learning, and/or glucose metabolism are preferred. Education and Experience Requirements: PhD in neuroscience
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Experience with molecular biology techniques and cell culture Willingness to learn new techniques and skills Ability to independently design and conduct experiments Willingness to think critically A passion